Clustering-based revision debug in regression verification
Bibliographic record
Abstract
Modern digital systems are growing in size and complexity, introducing significant organizational and verification challenges in the design cycle. Verification today takes as much as 70% of the design time with debugging being responsible for half of this effort. Automation has mitigated part of the resource-intensive nature of rectifying erroneous designs. Nevertheless, most tools target failures in isolation. Since regression verification can discover myriads of failures in one run, automation is also required to guide an engineer to rank them and expedite debugging. To address this growing regression pain, this paper presents a framework that utilizes traditional machine learning techniques along with historical data in version control systems and the results of functional debugging. Its aim is to rank revisions based on their likelihood of being responsible for a particular failure. Ranking prioritizes revisions that ought to be targeted first, and therefore it speeds-up the localization of the error source. This effectively reduces the number of debug iterations. Experiments on industrial designs demonstrate a 68% improvement in the ranking of actual erroneous revisions versus the ranking obtained through existing industrial methodologies. This benefit arrives with negligible run-time overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".